SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6051–6075 of 10420 papers

TitleStatusHype
Graph Classification with Geometric Scattering—0
Graph-based Representation for Image based on Granular-ball—0
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping—0
Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond—0
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models—0
Graph based Label Enhancement for Multi-instance Multi-label learning—0
An Efficient and Small Convolutional Neural Network for Pest Recognition -- ExquisiteNet—0
Lost in Translation: Modern Neural Networks Still Struggle With Small Realistic Image Transformations—0
Co-Training 2L Submodels for Visual Recognition—0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
A High-Performance Adaptive Quantization Approach for Edge CNN Applications—0
MinConvNets: A new class of multiplication-less Neural Networks—0
Co-training 2^L Submodels for Visual Recognition—0
Low-Memory Neural Network Training: A Technical Report—0
Graph Based Convolutional Neural Network—0
Low-rank features based double transformation matrices learning for image classification—0
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification—0
On the Robustness of Malware Detectors to Adversarial Samples—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification—0
Graph-Based Classification of Omnidirectional Images—0
Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks—0
Microscopic fine-grained instance classification through deep attention—0
Grafting Vision Transformers—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified